Background: In the past decades, big data (BD) has become a buzzword which is associated with the opportunities of gaining competitive advantage and enhanced business performance. However, data in a vacuum is not valuable, but its value can be harnessed when used to drive decision-making. Consequently, big data analytics (BDA) is required to generate insights from BD. Nevertheless, many companies are struggling in adopting BDA and creating value. Namely, organizations need to deal with the hard work necessary to benefit from the analytics initiatives. Therefore, businesses need to understand how they can effectively manage the adoption of BDA to reach decision-making quality. The study answers the following research questions: What factors could influence the adoption of BDA in decision-making? How can the adoption of BDA affect the quality of decision-making? Purpose: The purpose of this study is to explore the opportunities and challenges of adopting big data analytics in organizational decision-making. Method: Data is collected through interviews based on a theoretical framework. The empirical findings are deductively and inductively analysed to answer the research questions. Conclusion: To harness value from BDA, companies need to deal with several challenges and develop capabilities, leading to decision-maker quality. The major challenges of BDA adoption are talent management, leadership focus, organizational culture, technology management, regulation compliance and strategy alignment. Companies should aim to develop capabilities regarding: knowledge exchange, collaboration, process integration, routinization, flexible infrastructure, big data source quality and decision maker quality. Potential opportunities generated from the adoption of BDA, leading to improved decision-making quality, are: automated decision-making, predictive analytics and more confident decision makers.

The term Big Data has been gaining importance both at the academic and at the business level. Information technology plays a critical role in shipping since there is a high demand for fast transfer and communication between the parts of a shipping contract. The development of Automatic Identification System (AIS) is intended to improve maritime safety by tracking the vessels and exchange inter-ship information.  This master’s thesis purpose was to a) investigate in which business decisions the Automatic Identification System helps the shipowners and operators (i.e., users), b) find the benefits and perils arisen from its use, and c) investigate the possible improvements based on the users’ perceptions. This master’s thesis is a qualitative study using the interpretivism paradigm. Data were collected through semi-structured interviews. A total of 6 people participated with the following criteria: a) position on technical department or DPA or shipowner, b) participating on business decisions, c) shipping company owns a fleet, and d) deals with AIS data. The Thematic Analysis led to twenty-six codes, twelve categories and five concepts. Empirical findings showed that AIS data mostly contributes to make strategic business decisions. Participants are interested in using AIS data to measure the efficiency of their fleet and ports, to estimate the fuel consumption, to reduce their costs, to protect the environment and people’s health, to analyze the trade market, to predict the time of arrival, the optimal route and speed, to maintain highest security levels and to reduce the inaccuracies due to manual input of some AIS attributes. Finally, participants mentioned some AIS challenges including technological improvement (e.g., transponders, antennas) as well as the operation of autonomous vessels.  Finally, this master’s thesis contributes using the prescriptive and descriptive theories and help stakeholders to find new decisions while researchers and developers to advance their products.